论文标题

探索噪声和降解对心脏声音分类模型的影响

Exploring the Impact of Noise and Degradations on Heart Sound Classification Models

论文作者

Panah, Davoud Shariat, Hines, Andrew, McKeever, Susan

论文摘要

近年来,数据驱动的心脏声音分类模型的发展一直是研究的积极领域。首先要开发此类数据驱动的模型,需要使用信号采集设备捕获心脏声音信号。但是,由于大多数情况下存在内部和外部声音,几乎不可能捕获无噪声的心声信号。心脏声音信号中的这种噪音和降解可能会降低数据驱动分类模型的准确性。尽管文献中已经提出了不同的技术来解决噪声问题,但心脏声音信号中的不同噪声和降解如何影响数据驱动分类模型的准确性,但仍未得到探索。为了回答这个问题,我们产生了一个合成心脏声音数据集,包括正常和异常的心脏声音,这些声音被各种噪音和降解污染。我们使用该数据集研究了心脏声音记录中噪声和降解对不同分类模型的性能的影响。结果显示出不同的声音和降解在不同程度上影响心脏声音分类模型的性能。有些对于分类模型更有问题,而另一些则不那么破坏性。将这项研究的发现与我们先前与一组临床医生进行的调查的结果进行比较,表明对分类模型更有害的噪声和降解也更具破坏性对准确的听觉。可以利用这项研究的发现来开发针对性的心脏声音质量增强方法 - 根据心脏声音信号中噪声和降解的特征,可以适应质量增强的类型和侵略性。

The development of data-driven heart sound classification models has been an active area of research in recent years. To develop such data-driven models in the first place, heart sound signals need to be captured using a signal acquisition device. However, it is almost impossible to capture noise-free heart sound signals due to the presence of internal and external noises in most situations. Such noises and degradations in heart sound signals can potentially reduce the accuracy of data-driven classification models. Although different techniques have been proposed in the literature to address the noise issue, how and to what extent different noise and degradations in heart sound signals impact the accuracy of data-driven classification models remains unexplored. To answer this question, we produced a synthetic heart sound dataset including normal and abnormal heart sounds contaminated with a large variety of noise and degradations. We used this dataset to investigate the impact of noise and degradation in heart sound recordings on the performance of different classification models. The results show different noises and degradations affect the performance of heart sound classification models to a different extent; some are more problematic for classification models, and others are less destructive. Comparing the findings of this study with the results of a survey we previously carried out with a group of clinicians shows noise and degradations that are more detrimental to classification models are also more disruptive to accurate auscultation. The findings of this study can be leveraged to develop targeted heart sound quality enhancement approaches - which adapt the type and aggressiveness of quality enhancement based on the characteristics of noise and degradation in heart sound signals.

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